Google Cloud Professional Machine Learning Engineer — Question 141

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

Answer options

• A. Use Vertex Al Pipelines to execute the experiments. Query the results stored in MetadataStore using the Vertex Al API.
• B. Use Vertex Al Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
• C. Use Vertex Al Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
• D. Use Vertex Al Workbench user-managed notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

Correct answer: A

Explanation

The correct answer is A because Vertex AI Pipelines is designed to manage and automate ML workflows, making it suitable for tracking and reporting experiments with minimal manual effort. Options B and C involve storing metrics in BigQuery and Cloud Monitoring respectively, which may require more manual setup and are not specifically tailored for experiment tracking like Vertex AI Pipelines. Option D relies on Google Sheets, which can be less efficient for tracking numerous experiments compared to a dedicated system like MetadataStore.